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This paper addresses a real life scheduling problem characterised by multi-resource operations whose completion simultaneously requires more than one (renewable) resource of different types. Such problems arise in various companies not only in manufacturing but also in services. Solving such a problem needs to address two interconnected subproblems: a sequencing subproblem and a resource-allocation subproblem if more than one resource is available in some types. The resource-allocation subproblem consists of allocating to each operation the resources it requires while the sequencing subproblem consists of determining the order in which each resource performs the operations assigned to it. This paper focuses on the resource-allocation subproblem. It generalises the basic scheduling problems which consist of determining the operations' starting or completion times for a given processing sequence for every resource. We consider a cost function taking into account the makespan, the cost of resource utilisation, and the load imbalance among the resources of the same type. We first formulate the problem into a mixed-integer linear program (MILP). To efficiently solve it, even in practical-sized instances, an exact algorithm called BD (Benders decomposition with enhancing cuts) is developed where the master problem only considers integer variables. We prove that the slave problem can be transformed into finding the longest paths in a digraph and therefore can be solved with the Bellman–Ford algorithm. To enhance the efficiency of the method, equivalent solutions are limited in the master problem. The performance of the approach is evaluated by comparing it against CPLEX, a state-of-the-art commonplace MILP solver, used to directly solve the initial MILP. The computational results demonstrate that BD provides competitive solutions in all upper and lower bounds. In particular, it improves, compared with CPLEX, the upper and lower bounds by 5.07% and 4.63%, respectively, in solving practical-sized instances. The experiment also shows that considering load balancing can make more rational use of resources and avoid adverse effects caused by excessive workload of staff and imbalanced use of equipment, which is very important in real-world production. [ABSTRACT FROM AUTHOR]

Information barriers exist between subsidiaries in a group enterprise, where each subsidiary acts independently, and hinder rational allocation and utilisation of resources. This study proposed a value co-creation model consisting of heterogeneous subsidiaries in a group enterprise and consumers. By constructing a three-party evolutionary game model, we discussed the impact of information leakage risk, punishment intensity, consumer experience, and reputation on the strategy evolution process, which means the proportion of strategy choices for the three entities over time. We analysed and verified the evolutionary process of the value co-creation system in different scenarios. This study revealed the 'butterfly effect' of value co-creation within the group enterprise, highlighting how different combinations of decision-making probabilities among the three entities in the initial stage can result in diverse outcomes, even when starting from identical conditions. The study also found that under certain conditions, group subsidiaries may sacrifice their own interests for the overall interests of cooperation. This study yielded the counterintuitive conclusion that in a stable state, the choice of cooperation strategies between subsidiaries is not directly related to their willingness to share knowledge. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where service composition (SC) is a critical process concentrating on matching tasks and services. Existing studies usually ignore the dynamic nature of the CMfg environment, where task information is not always known before. Moreover, CMfg services are re-entrant, i.e. after being occupied for a period of service time, these services re-enter the CMfg platform (i.e. be available again). Re-entrant services significantly complicate CMfg platform revenue management. In this regard, we study the dynamic SC problem of CMfg (CMfg-DSC) incorporating re-entrant services within an online setting for the first time. CMfg-DSC is reformulated as an online packing problem. If a task is accepted, each requested service will be occupied until service time terminates. We propose online policies with performance guarantees, namely, static online packing policy (Static), opportunity-cost-based policy (Oppo), and dual-based policy with/without known distribution (Dual-k & Dual-u). Experiment results show that (1) Static is applicable for most cases; (2) Oppo has the potential for decent performance but at the cost of time; (3) Dual-u is reliable when only past observations are available; (4) Dual-k performs well given abundant service provision, but its performance would deteriorate if we lower the reward-cost threshold. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput. [ABSTRACT FROM AUTHOR]

Human–robot collaboration has been incorporated into production and assembly processes to promote system flexibility, changeability and adaptability. However, it poses new challenges to resource allocation and production scheduling due to its intrinsic uncertainty and also the increasing complexity of resources. This paper investigates a stochastic flow shop scheduling problem in the context of human–robot collaboration. The goal is to achieve efficient utilisation of flexible resources including human workers and cobots and take full advantage of human–robot collaboration in production scheduling. A stochastic Cobb–Douglas production function is utilised to evaluate the production efficiency of human–robot collaboration considering instabilities of human performance. A joint chance-constrained programming model is formulated to ensure that the required system performance can be achieved. A CVaR approximation-based approach is proposed to solve the formulated model with mixed-integer variables and a nonconvex constraint. The effectiveness of the formulated model and the efficiency of the proposed solution approach are evaluated via numerical experiments. Computational results show the superiority of our solution approach over three other approaches including Bonferroni approximation, scenario approach and individual chance-constrained programming. [ABSTRACT FROM AUTHOR]

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey--Clarke--Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application. [ABSTRACT FROM AUTHOR]

Since its launch in November 2022, ChatGPT has pioneered a new era in AI, globally acclaimed for its content creation and language understanding. This advancement is reshaping industries like hospitality, offering innovative applications but also raising ethical and efficiency challenges. In the context of New Zealand's hotel industry, renowned for its vibrant and inclusive 'Kiwi' hospitality culture, the idea of incorporating generative AI offers a novel perspective. While its application could potentially enhance service efficiency and help to alleviate staff shortages, integration with the country's deeply rooted cultural values demands a carefully considered approach. This study adopted a qualitative methodology using semi-structured interviews with hotel managers and AI experts in New Zealand. The findings revealed that generative AI holds promises for cost savings, work efficiency and meeting specific social group demands. Concerns have been raised, however, relating to the ability of AI to handle complex interactions, incorporate a sense of Kiwi culture, respond appropriately to service contingency events, maintain data privacy and meet the generational service preferences of guests. The absence of clear AI legislation has led to cautious interest among hotel managers, restrained by concerns around legality, privacy and service quality. [ABSTRACT FROM AUTHOR]

In recent years, the high cost of governance in Nigeria has been a major concern for policymakers and citizens alike. This has had a significant impact on the country’s socio-economic development. As a result, Nigeria has struggled to achieve significant socioeconomic development, despite its vast resources and potential. In light of this, this paper conducted a thorough analysis through documentary sources of data to investigate the factors contributing to the high cost of governance and the impact it has on Nigeria’s socioeconomic development. The paper found that corruption, inefficient resource management, and excessive bureaucracy are the primary reasons for the high cost of governance in Nigeria. These factors not only increase government spending but also hinder the efficient allocation of resources. Furthermore, the study found that high cost of governance has resulted in a significant budget deficit and a heavy debt burden for the country. In conclusion, the paper recommends among others the urgent need for the Nigerian government to address the high cost of governance. By addressing corruption, improving resource management, and reducing bureaucracy, the government can lower its spending and allocate resources more efficiently, ultimately contributing to the country’s socio-economic development. [ABSTRACT FROM AUTHOR]

Wildfires exert a destructive influence on forest ecosystems. Precise positioning of the fire margin (large-scale fireline) of extensive wildfires facilitates the allocation of firefighting resources. However, the direct positioning of the entire fire field by a single unmanned aerial vehicle (UAV) remains a formidable challenge due to the extensive horizontal expanse of large-scale firelines, and asynchronous monitoring of the entire fire field results in suboptimal accuracy as a consequence of the dynamic propagation of the fireline. To address these limitations, this paper presents a global positioning method for large-scale firelines employing a single UAV. By asynchronously locating part fireline through the UAV's airborne fireline real-time positioning system during UAV flight, the acquired asynchronous firelines are synchronized to the same time utilizing a fire spread prediction model, ultimately achieving global positioning of the firelines. The efficacy of the proposed method was validated using FARSITE-simulated wildfire data and two field-prefabricated wildfire experiment datasets. The outcomes demonstrate that the proposed method outperforms direct asynchronous positioning of the entire fireline by a single UAV in terms of accuracy. [ABSTRACT FROM AUTHOR]

The economic effects of educational mismatch include its negative impact on economic growth, which is due to the inefficient allocation of resources it generates. The main causes of this phenomenon need to be determined to correctly implement policies to avoid such mismatches. This paper investigated the determinants of educational mismatch from a gender perspective by analysing its sectoral heterogeneity among the four key productive branches within the tourism sector. We employed a multinomial logit model using data from the 2018 Spanish Wage Structure Survey. The results highlight the higher probability of male overeducation in the hospitality industry and the predominance of female overeducation in the other services analysed. Likewise, there was a lower probability of male undereducation in the transport and other complementary services sectors. As a result of these findings, we propose various corrective measures to redirect specific training efforts for each of these groups, thus promoting gender equality. [ABSTRACT FROM AUTHOR]

While platform protection insurance (PPI) is extensively employed as an insurance policy in many sharing platforms, research on its influence on the tourism supply chain is lacking. At this point, the quality of online tourism services has taken the brunt of consumer complaints, and new solutions are desperately needed. So, would PPI's involvement increase the quality of online tourism services? In this paper, by constructing an online tourism supply chain consisting of a Tourism Supplier (TS), an Online Tourism agency (OTA), and consumers, we study the conditions of PPI introduction and its impact on online tourism service quality and analyze the behavioral decisions of online tourism supply chain subjects. The study found that in the tourism market with a small amount of demand change, OTA is more willing to introduce PPI, and TS is more inclined to improve service quality, which then lowers the service cost of TS and the level of disclosure of information, the stronger the willingness of OTA to introduce PPI, and the introduction of PPI in the lower PPI penetration rate of the tourism market has a more promotive effect on the TS with lower service cost. [ABSTRACT FROM AUTHOR]

Academics and practitioners have identified the potential of Big Data Analytics capability (BDAC) and Lean Social-Technical system to improve sustainability performance. Nonetheless, there is still a limited understanding of how companies' BDAC efforts can contribute to Lean Practices and transform this relationship into sustainability-related benefits, impacting economic, social, and environmental performance. A comprehensive conceptual model to assess the mediating effect of Lean Social Practices (LSP) and Lean Technical Practices (LTP) on the relationship between BDAC and economic, social, and environmental performance was developed and tested with a sample of 108 respondents, from Brazilian industrial companies. The results obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), showed that the relationship between BDAC and economic performance is completely mediated by LTP. However, LSP does not mediate the relationships between BDAC and sustainability performance. The findings provide guidance to companies regarding resource allocations for BDAC and Lean to foster sustainability. [ABSTRACT FROM AUTHOR]

Cyber-Physical System (CPS) focuses on complex interdependencies and integration between cyberspace and the physical world. CPS holds significant potential in aiding construction professionals, encompassing Quantity Surveyors (QSs). Therefore, facilitating QS's role through CPS is pivotal to meeting the competitive advantage in construction. Thus, this study aims to investigate the adaptability of CPS to facilitate the role of QSs. The research employed a qualitative method, encompassing three Delphi rounds, incorporating semi-structured expert interviews. The data were analysed using NVivo12. It empirically proves the potential of CPS to facilitate the role of QSs. Findings revealed 24 barriers hindering CPS implementation and 11 enablers for CPS implementation. The study concluded with 22 strategies to minimise barriers and expedite the implementation of CPS among QSs. As there is limited research on adaptability of CPS for QSs, the findings can enhance the awareness and practical implementation of CPS by intervening to form more partnerships among application developers and industry leaders. The paper offers valuable insights into the successful CPS implementation, allowing for enhanced project management, improved efficiency, and optimized resource allocation as the contribution to the industry. This research uncovers several further research areas, including longitudinal studies on CPS to facilitate the roles of QSs. [ABSTRACT FROM AUTHOR]

Increased terrorist propaganda has caused greater violence worldwide. Communities are efficient channels through which terrorist organizations can access target audiences; thus, communities are playing larger roles in terrorist propaganda distribution. However, no previous studies have examined the interactions between terrorists and governments regarding terrorist propaganda in communities from a game-theoretic lens. This article aims to fill this research gap. We first classify the relationships between the communities as independent or interactive. For both cases, we describe the process of terrorist propaganda distribution in communities, highlighting the dependence of the degree of terrorist ideology within a community on the terrorist-organization and government resource allocations; based on this, we build novel sequential game models of the terrorist and the government and obtain the equilibrium solutions. Furthermore, we conduct a comparative study to elucidate the differences in the expected government losses for independent and interactive communities. The outcomes are as follows: (i) the higher the community propagation centrality, the greater the resource expenditure of the government and terrorist organization, and (ii) introducing an interaction network among communities decreases the expected government loss when the network directly destroys the distribution uniformity of the community propagation centralities. Finally, we present a case study to illustrate the usability of the proposed models. This study provides novel insights for governments targeting optimal allocation of intervention resources to communities so as to counter terrorist propaganda distributed by a strategic terrorist. [ABSTRACT FROM AUTHOR]

This article examines a real-time parking reservation service that dynamically allocates a limited number of parking slots (which can be reused) to serve parking requests in a crowded area. The study takes into account two stochastic factors: (i) random arrivals of parking requests and (ii) driver parking unpunctuality, which are seldom simultaneously considered in the existing literature. This study formulates the dynamic parking resource allocation problem as a joint chance-constrained model to maximize the expected total revenue over a finite horizon. Due to the complex nature of the model considering the two stochastic factors, the study proposes a data-driven approach using joint chance constraint decomposition and sample average approximation to approximate the model to a deterministic mixed-integer programming model. Furthermore, a stratified ranked set sampling method is introduced to construct a high-quality sample set as the input to the deterministic model, and valid inequalities are designed to accelerate the solution process. Numerical experiments based on real-world data are conducted to validate the performance of the proposed approach under multiple parking scenarios and provide findings. [ABSTRACT FROM AUTHOR]

Based on a review and synthesis of literature on Hybrid Business Models (HBMs) and the sharing economy (SE), this study advances a conceptual framework for HBMs in the context of the SE. The study sheds light on key research themes within the domain of HBMs, encompassing value proposition, governance and coordination, resource allocation, sustainability, reputation building, communication channels, and key sharing ecosystem partners. These models integrate elements such as access, platform, and the community-based economy, which are crucial for SE dynamics. This integration represents the best of both approaches, creating a balanced strategy and strengthening overall business operations The managerial implications, including the need for managers to leverage information technology for developments, are identified and outlined. [ABSTRACT FROM AUTHOR]

The article focuses on experimental research designs used to evaluate interprofessional education and collaborative practice. Topics include the methodologies for assessing the effectiveness of interprofessional education, the impact of collaboration on healthcare outcomes, and the challenges in conducting experimental research in healthcare settings.

This paper investigates the effect of land scale heterogeneity on participation by farmers in collective action and the level of erosion control, and its mechanism of action, using survey data from 45 villages and 1,149 households in a typical erosion area of the Yellow River basin. The results show that there is a non-linear, U-shaped relationship between land size heterogeneity and farmers' participation in collective action and the level of erosion control, respectively, and that collective action plays a mediating role in the U-shaped relationship between land size heterogeneity and level of erosion control. This implies that maintaining a homogeneous distribution of land among households within the village collectivity and a large disparity in land allocation both promote participation in collective action and thus improve the level of erosion control. Using an endogenous regression transformation model to deal with endogeneity and robustness tests, the findings remain robust. In the mechanism analysis section, variables for the level of trust and co-ordination of interests are introduced and a non-linear, U-shaped relationship is found between land size heterogeneity through the effect of both on collective action. This suggests that the allocation of land resources as described above can co-ordinate the level of trust and the direction of interests among farmers, leading to conditions for co-operation and thus facilitating farmers' participation in collective action. [ABSTRACT FROM AUTHOR]

The Wraparound approach aims to prevent and interrupt gang activity by "wrapping" youth with individualized supports to increase prosocial connections and reduce the risks for gang involvement. The current study examines 38 interviews with program staff and external stakeholders to assess implementation strengths and challenges of a Wraparound program in British Columbia, Canada. Thematic analysis resulted in seven strengths-related themes concerning program structure, team, and youth engagement strategies, and seven challenge-related themes regarding youth interactions, resources, and communication. Findings highlight the benefits of an individualized approach, with consideration of enhanced communication and outreach strategies, resource allocation, and case manager training. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has revolutionized production methods through automated and intelligent manufacturing. Using Chinese A-share listed companies from 2007 to 2015, we conduct a variety of regressions and find that AI improves export product quality. Moreover, AI enhances export product quality by boosting resource allocation efficiency and information-processing abilities. Compared with the collaborative intelligent manufacturing mode, the quality-driver effect of AI is only significant in the production-based intelligent manufacturing mode. Our findings suggest that emerging countries should motivate their exporters to adopt intelligent manufacturing methods to enhance product quality. [ABSTRACT FROM AUTHOR]

This article examines the localization of digital governance technologies in inland China, with a focus on Guizhou Province, which houses China’s first big data pilot zone. Despite the centralization efforts of the Xi Jinping administration, local agents are pivotal in policy implementation, particularly in regions far from political and economic hubs. Combining studies of policy implementation and concerns about the materiality of information technology, this research problematizes the localization process of digital governance. It introduces the analytical framework termed the “frame of locality” to emphasize the dynamic interplay between national digital governance policies and local socio-economic and cultural contexts, indicating that the resolution of implementation frictions in various ways is crucial for policy execution. With the case of Guizhou, the research uncovers an inherent contradiction: the local transformation of digital governance can be hindered by its own implementation processes, thus challenging the notion of re-centralizing social governance through digital technologies. This can both challenge centrally-designed sociotechnical visions and provide opportunities for more inclusive local resource allocation and for policymakers to reassess and adjust to local conditions. The study contributes to the broader understanding of China’s digitization efforts by highlighting the significance of local contingent dynamics in accomplishing centrally planned objectives. [ABSTRACT FROM AUTHOR]

The role of gender stereotypes in students' academic performance attracts increasing attention among economists. In this paper, we explore how gender stereotypes among parents can affect students' maths performance. Using the data on junior high school students from the China Education Panel Survey, we find that when parents believe boys to be better than girls in maths, such a gender stereotype results in perceived difficulty in maths and poor maths performance among girls. Parental gender stereotyping does not change their allocation of educational resources, but rather shapes girls' gender stereotypes and thereby lowers their academic performance. In contrast, we do not find any significantly negative effects of parental gender role stereotypes among boys. [ABSTRACT FROM AUTHOR]

Taking the implementation of China's Stock Connect program as a quasi-natural experiment, we use the difference-in-differences (DID) method to document the relationship between the capital market opening up and corporate green technology innovation from 2007 to 2020. The results show that the capital market opening up significantly promotes corporate green technology innovation by reducing financing constraints and raising firms' environmental protection awareness. However, this effect is more pronounced in non-state-owned enterprises and enterprises with higher initial productivity, higher regional marketization degree, or fiercer industrial market competition. Additionally, by improving corporate green technology innovation, the capital market opening up can further promote the total factor productivity of firms. Using evidence from China, we verify from the micro level that the capital market opening up is crucial for developing economies in their sustainable development. [ABSTRACT FROM AUTHOR]

Since 2002, with the SARS outbreak, infectious diseases, including the ongoing COVID-19 pandemic, have continued to be a major global public health threat. It is critical to develop effective data science methods to quickly detect disease outbreaks and contain their rapid globalized spread. However, in practice, limited testing availability, and thus insufficient testing data poses significant challenges in effective analysis and real-time monitoring of infectious diseases, especially during early stages of a novel disease outbreak. To tackle these challenges, this article proposes adaptive testing resource allocation strategies integrated with a physics-informed model to dynamically allocate limited testing resources across different communities. The physics-informed model accounts for transmission dynamics and health disparities, enabling effective health risk assessment despite limited data. By integrating nonstationary Multi-Armed Bandit techniques that strike superior balance between exploring the communities with high uncertain risks and exploiting those with high risk levels, the proposed methodology facilitates test allocation to collect high-quality testing data for early outbreak detection. Theoretical analysis is carried out to evaluate the performance of the proposed allocation strategies, ensuring either sublinear or linear dynamic pseudo-regret under regularity assumptions. A comprehensive simulation study is conducted under three transmission scenarios to thoroughly evaluate the proposed methodology. [ABSTRACT FROM AUTHOR]

Planning and allocation of resources in search and rescue operations is critical and complex. Those tasks require consideration of several factors such as the nature of the emergency, location, people involved, and weather conditions. In this paper, we investigate the specific requirements for effective decision-making and planning in search and rescue operations in Norway. These requirements were determined using data gathered through expert interviews and analysis of mission reports. We propose a framework designed for search and rescue decision support, including its architecture and components. We also discuss the implementation of the service layer. We used information retrieval methods, i.e. BM25 and TF-IDF, for distance computation and compared their performance using k-means and hierarchical agglomerative clustering methods. The evaluation through silhouette score and Davies-Bouldin score shows that the hierarchical agglomerative clustering using BM25 performs well for the given dataset. The search and rescue mission reports published by the Norwegian Safety Authority were used as a dataset. In addition, the overall framework is evaluated by the domain experts using qualitative analysis. Finally, we discuss potential framework implementation challenges and provide suggestions for future research directions, in addition to presenting insights from the framework design process. [ABSTRACT FROM AUTHOR]

Logistics resource allocation depends on the precise scheduling of Supply Chain (SC) agents. Coordination of management across all sites, products, and production divisions is essential for effective scheduling. For multi-agent systems in heterogeneous SCs, it is crucial to have a prior understanding of production, delivery, and connectivity. Hence, an innovative meta-heuristic optimization inspired by sparrow behavior is introduced as multi-agent-based scheduling and resource allocation (MA-SRA) to resolve delivery delays and errors during delivery in logistic SC management. Allocating resources efficiently and creating workable schedules in an SC with multiple agents are the primary significant problems focused on in this research. The MA-SRA algorithm provides an achievable solution to the problem of optimizing logistics operations by combining precise scheduling with production balance and multi-agent searchers. If the scheduling operations are inadequate, sparse agents are repurposed for production based on fitness. This maintains balance and connectivity by adjusting agent ratios. Delays are minimized, and connectivity is maximized because no adjustments need to be reversed. The research findings show that the proposed approach improves operational efficiency and brings significant advantages to the industry in terms of enhanced allocation of resources, connectivity, delivery efficiency, and fewer delays and scheduling errors. [ABSTRACT FROM AUTHOR]

This study investigates the application of heuristic and meta-heuristic algorithms to address resource allocation challenges in Internet of Things (IoT) applications within fog computing environments. The primary advantage of these algorithms lies in their ability to optimize functions without the need for stringent restrictions, allowing adaptability to various linear, nonlinear, continuous, or discrete problems. Through the implementation and comparison of the Cheetah algorithm, Gray Wolf algorithm, Particle Swarm-Gravitational Search algorithm, and Gray Wolf-Cuckoo Search algorithm using MATLAB software in a simulation environment, the study aims to minimize criterion function and total time and energy consumption for IoT applications. Preliminary results indicate that the statistical average performance of the Cheetah algorithm surpasses that of the Gray Wolf algorithm, the combined Particle Swarm-Gravitational Search algorithm, and the Gray Wolf-Cuckoo Search algorithm. This suggests the efficacy of the Cheetah algorithm in IoT resource allocation optimization within fog computing environments. The study provides insights into the comparative performance of these algorithms, laying the foundation for further exploration into enhancing resource allocation strategies in the dynamic and resource-constrained IoT and fog computing landscapes. [ABSTRACT FROM AUTHOR]

With the growth of the digital economy, the strategic significance of the smart city pilot policy to 'empower' the digitalisation of enterprises has become prominent. This study examines the impact and mechanism of the smart city pilot policy on the digitalisation of enterprises using the difference-in-differences (DID) method. Findings show that (1) the digitalisation level of the enterprises in a pilot city exceeds that of the enterprises in a non-pilot city, demonstrating that the policy can facilitate enterprise digitalisation improvement; (2) the smart city pilot policy encourages enterprise digitalisation in two ways: technological innovation and optimal resource allocation; and (3) the heterogeneity test reveals that the policy has a promotion effect on the digitalisation of enterprises that belong to an industry that is related to the smart city, state-owned enterprises, and those located in the eastern region. This study has significant policy ramifications that may help enterprises further develop their digitalisation. [ABSTRACT FROM AUTHOR]

Flight feather moult is an energetically expensive stage of the annual cycle of birds. Its timing is adjusted to other important time- and energy-demanding activities, including migration. In the vast majority of migratory birds, primary moult occurs before or after migration or moulting is suspended during migration. The Black-headed Gull is an exception: it starts moulting flight feathers at the onset of the autumn migration. The course of primary moult in this species is still poorly understood due to the difficulty in obtaining sufficient data from the entire period of flight feather replacement. In this paper, we employed digital photos taken in the field to estimate the start, variation in the start date and duration of primary moult in adult and immature Black-headed Gulls, by using the Underhill–Zucchini likelihood moult model. On average, immatures started their moult on 7 June, 25 days earlier than adults, but their moult lasted 5 days longer. Consequently, there was a 20-day difference in the end of moult – that is, on average it occurred on 16 September and 6 October in immature and adult gulls, respectively. Adults showed greater variability in the mean primary moult start date than immatures, as the beginning of flight feather replacement may depend on breeding success (earlier after breeding failure) and on breeding phenology. The overlap of primary moult and migration produces a trade-off in resource allocation between those two processes, which leads to a low number of simultaneously growing primaries and a decrease in the growth rate of the two external, heaviest flight feathers. Existing reports of a later completion of primary moult in the first half of November in this species are based on the faulty assumption that the outermost primary is always the longest, which is not the case in 34% of individuals. [ABSTRACT FROM AUTHOR]

Due to technological advancements, most devices are generating a significant amount of data which needs appropriate technology to handle the data generated by IoT devices. Fog computing addresses this challenges in a decentralised manner. This paper proposes an efficient resource allocation and management strategies with energy efficiency (ERAM-EE) to effectively allocate available resources in Fog-enabled networks. The ERAM-EE algorithm utilises the channel gain matrix of the interconnected network to assign IoT devices to Fog nodes (FNs) through resource blocks (RBs) with three stages. In the initial stage, one FN is assigned to each IoT device through a single RB by calculating the maximum value of the channel gain. In the subsequent stage, the remaining RBs are assigned to unassigned FNs for future task-offloading processes. Finally, the unassigned RBs are allocated to IoT devices by calculating the maximum channel gain of the Fog–IoT networks. Simulated results indicate that the ERAM-EE scheme confirms that each IoT device is mapped with minimum one FN and RB for effective task scheduling and resource management. Analysis reveals that the ERAM-EE method achieved an increase in EE of up to 7, 8, and 18 Mbit/J compared to existing schemes for varying IoT devices, FNs and RBs respectively. [ABSTRACT FROM AUTHOR]

Non-Orthogonal Multiple Access (NOMA) and Fractional Frequency Reuse (FFR) were touted as two key techniques of the millimetre wave cellular system to derive spectrum efficiency and user performance improvement. By combining the NOMA and FFR techniques, this paper proposes a system to improve the performance of the NOMA user that utilises the NOMA-power domain technique to reuse an occupied Resource Block (RB). Besides following the power allocation policy of the NOMA technique, the resource allocation algorithm for the NOMA user is based on the FFR technique. Particularly, if the downlink Signal-to-Interference-plus-Noise ratio (SINR) of the NOMA user is greater than the threshold, it is allocated the Cell-Edge RB with a high interference level. Otherwise, it is served on the Cell-Center RB with a low interference level. The analytical and simulation results state that the proposed system can increase the coverage probability of the NOMA user up to 250%. [ABSTRACT FROM AUTHOR]

We present a Defense/Attack resource allocation model, where the Defender has some number of 'locks' to protect n vulnerable boxes (sites), and the Attacker is trying to destroy these boxes, having some number of 'bombs' which can be placed into the boxes. Similar models were studied in game theory – e.g. (Colonel) Blotto game, but our model has a feature that is absent in the previous literature. Here, the Attacker tests the vulnerability of all sites before allocating her resources. However, these tests are not perfect, i.e. a test can give a positive result for a box without a lock and a negative result for a box with a lock. We give a full solution for some important special cases of the general Locks-Bombs-Testing (G-LBT) model, where sites have distinct values. These results complement previously known results for a symmetrical LBT model, where all parameters are the same for each box. We also outline an approach to a general solution for the G-LBT model. [ABSTRACT FROM AUTHOR]

To investigate the relationship between Chinese university funding and academic output, we collected data on the scale and the structure of funding and academic output of 36 World-Class universities under construction in China during 2013–21 and 2014–22. Furthermore, we empirically analyzed the effects of funding scale and structure on academic output using ordinary least squares (OLS) regression, random effects, and fixed effects models. This study demonstrates that the scale and structure of funding have a significant effect on academic output. Among them, the scale of total budget revenue has a significant effect on academic output, and its influence on highly-cited researchers (HiCi) is greater than the other indicators. The scale of total budget expenditure also has a significant positive influence on academic output, and its influence on N&S is greater than the other indicators in order. The structure of revenue has a significant influence on academic output, and business revenue has the greatest influence on PUB. The structure of horizontal expenditure has a significant influence on academic output. The vertical expenditure structure has a significant impact on academic output, and educational expenditure has the greatest impact on PUB. Based on the results, we discuss implications for researchers and for policymakers to improve the construction effect of World-Class universities under construction in China. [ABSTRACT FROM AUTHOR]

The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively. [ABSTRACT FROM AUTHOR]

Many definitions of degrowth highlight that the corresponding great and deep transformations are going to be "designed," "planned," and "democratic." However, critical issues of democratic planning have only recently begun to be discussed. Considering a range of aspects of the technical and social division of labor, the article first revisits some of the main issues of market vis-à-vis planned resource allocation in capitalist and socialist economic growth contexts. It then zooms in on goals, characteristics, and likely issues of division of labor and resource allocation in planned degrowth circumstances. I argue that degrowth societies could use three measures to reduce the complexity of the division of labor that undermined socialist planning attempts. First, degrowth societies could immediately phase out the "excess sector" of production. Second, they could develop, under consideration of their institutional national traditions, pragmatic mixes of ex ante planning (assisted by digital solutions) of the "essential" economic sector and ex post or market regulation of the "in-between" sector. Finally, a parsimonious use of digital tools is likely to be helpful in connecting different scales of governance and associated planning activities (local, national, regional, global). To democratically legitimize planning goals and processes without overburdening people, pathways may be sought that emphasize value over formal rationality, or general planning principles and strategies over concrete targets and tasks. Democratic legitimacy could further increase through a complementation of representative democracy through deliberative instruments such as citizen forums or assemblies as well as applications of the subsidiarity principle. [ABSTRACT FROM AUTHOR]

As the digital economy continues to grow, data resources are becoming increasingly important as factors of production. The existing body of literature lacks comprehensive research on how digital data actually becomes a production factor. This study aims to fill that gap by examining how data assets affect corporate cost stickiness. Our findings show that data assets have a significant impact on reducing cost stickiness, and this conclusion remains robust even after conducting various rigorous tests. Mechanism tests indicate that data assets help companies in effectively managing the bullwhip effect resulting from demand fluctuations in the supply chain. Moreover, companies that possess and utilize data assets have better inventory turnover, enhancing their capacity for flexible production. Furthermore, the inhibitory effect of data assets on cost stickiness is more pronounced in regions characterized by a high level of digital economy development and among enterprises facing higher operational uncertainty. This highlights the important role of data assets as novel production factor in managing costs and allocating resources effectively. In conclusion, this research demonstrates the value of data assets in cost management and resource allocation for businesses in the digital era. [ABSTRACT FROM AUTHOR]

Inverse data envelopment analysis (DEA) method is a useful tool for analyzing the optimization of resource allocation of a set of homogeneous decision-making units (DMUs). However, the assumption of homogeneity for DMUs is not necessarily applied in reality. For example, universities with the same input structure cultivate students with different orientations; power plants consume different resources to generate electricity. Therefore, how to handle nonhomogeneous DMUs in inverse DEA becomes an issue when analyzing the optimization of resource allocation. In this study, an inverse DEA method for nonhomogeneous DMUs under constant returns to scale (CRS) is proposed to handle the aforementioned issue. Furthermore, the inverse DEA method for nonhomogeneous DMUs is extended to the case of variable returns to scale (VRS). The adjustment range of inputs-outputs under VRS is discussed to avoid the problem of infeasibility, and relevant properties of our method are proved. Moreover, the effect of frontier changes on the proposed method is also discussed. Finally, numerical examples are provided to illustrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

A manager's prior experience may affect an organization's resource allocation decisions and its performance. I examine this issue in the context of National Football League (NFL) franchises. I find that teams with coaches from an offensive background spend 5.5% points more of their salary cap space on offensive personnel, while a defensive background does not significantly affect the share of resources allocated to defensive personnel. While additional salary resources committed to offensive and defensive personnel do improve team performance in those respective areas, marginal improvements in offensive and defensive performance associated with greater spending are generally unaffected by the coach's background, a finding suggesting that a coach's background does not confer an informational advantage on certain types of personnel. I further find no evidence of significant differences in the effects that spending on offence and defence respectively have on team performance, suggesting efficiency within this labour market. [ABSTRACT FROM AUTHOR]

This paper investigates the relationships between challenges faced by Brazilian companies in adopting Energy Management Systems (EnMS). The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is employed to examine the interdependencies between the challenges. The study involves assembling an expert panel to assess the causal relations between key challenges identified through a literature review. The findings reveal that challenges related to insufficient financial resources, deficiencies in competencies within the implementation team, and lack of support from top management are pivotal factors influencing the adoption of EnMS. These challenges are positioned as core factors, highlighting their critical role in EnMS implementation. Conversely, factors such as difficulty in determining appropriate performance indicators and justifying EnMS certification maintenance are categorised as impact factors, signalling their lower priority for resource allocation. The study contributes to research by providing a nuanced understanding of the challenges hindering EnMS adoption and their interdependencies. Additionally, the findings offer practical insights for organisations seeking to implement EnMS, guiding resource allocation and strategic planning efforts to overcome barriers effectively. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of resilience concepts, methodologies, metrics, and modeling techniques, and identifies key research gaps. These include the lack of multilayer network representations of transportation infrastructures when quantifying resilience, and insufficient metrics for assessing equitable outcomes. To address these gaps, we propose a conceptual multilayer framework for transporttaion infrastructure networks and develop equitable resilience metrics that incorporate socioeconomic and demographic variables. Using Pioneer Valley as a case study, we apply our framework to assess the resilience of transportation networks and demonstrate its utility in guiding resource allocation to enhance both the physical robustness and social equity of infrastructure. Our findings show that this approach significantly contributes to more informed and inclusive decision-making, fostering the development of resilient and equitable urban environments. [ABSTRACT FROM AUTHOR]

Recreational spaces like parks and playgrounds play a vital role in community development. They serve as avenues for important experiences like physical activity, social interaction, and shared cultural expressions. However, BPoC (Black Person, and Person of Color) communities face disparate unjust environmental barriers to utilizing such spaces. Yet not much is known about how BPoC professionals in dedicated professional associations, could address such barriers through the pursuit of environmental justice when planning and designing recreational spaces. This study aims to address this lacuna in research. Findings from in-depth interviews demonstrate that in the design and planning of spaces for recreation, BPoC design professionals drew on their lived experiences from community history and personal encounters to: (1) foster opportunities for participation in design and planning processes (procedural justice), (2) address location-related concerns pertaining to systemic strains in resource allocation (distributive justice), and (3) integrate features that support socio-cultural relationships (interactional justice). [ABSTRACT FROM AUTHOR]

This paper focuses on the impact of return decisions on the bullwhip effect in the online closed-loop supply chain. We investigate the optimal return modes when the inspection system is undertaken by the logistics centre or the remanufacturer. Then, we optimise the online retailers' return decisions of minimising the inventory cost and bullwhip effect under different supply chain circumstances. Distinctive from previous conclusion that the inspection operation is usually undertaken by the remanufacturer to sort the returned products, the analysis results reveal that the optimal decision of mitigating the bullwhip effect in most cases is to set the inspection system on logistics centre. Besides, consumers' return and exchange behaviours in e-commerce have different impacts on the supply chain efficiency. The product return can not necessarily mitigate the information distortion in online closed-loop supply chain. The research provides insights for managers to strategise about return policies and resource allocation in different e-commerce contexts. [ABSTRACT FROM AUTHOR]

Industry 4.0 concepts make it possible to rethink human resources allocation, even for more traditional environments like metal machining. While parts machining on Computer Numerical Control (CNC) machines is automated, some manual tasks must still be executed by operators. The current approach is typically that operators are statically allocated to one or many machines. This causes avoidable bottlenecks. We propose an optimisation model to dynamically assign tasks to the operators with the objective of minimising production delays. Three different scenarios are compared; one representing the current widely used static allocation method and two others that allow more flexibility in the operators' allocation. The dynamic task assignment problem is solved using a constraint programming model. The model was applied to a case study from a high-precision metal manufacturing job shop. Experimental results show that switching from a static allocation to a dynamic one reduces by 76% the average production delays caused by human operators. Supposing more versatile operators under the dynamic allocation leads to further improvements. [ABSTRACT FROM AUTHOR]

Non- Orthogonal Multiple Access (NOMA) is one of the evolving and potential multiple access schemes in wireless networks to improve connection, spectrum efficiency, cell-edge throughput, and user fairness. As Spectral Efficiency (SE) is an important factor in data transmission to the user, in this paper the Least Sub-carrier Sum-Gain-based User Assignment Algorithm (LSSGUA) and Inter- and Intra-level Power Allocation (PA) algorithms for the Multi-Carrier (MC) Down-Link (DL)-NOMA are proposed to enhance the SE and allocate power to each sub-carrier/user. In LSSGUA, the channel gain of each sub-carrier/sub-channel is added and the sub-carrier with the least channel gain is given priority. In the case of the inter-level PA algorithm, the gain of the assigned users of each sub-carrier is added and the power allocated to each sub-carrier is in an ascending manner so that the sub-carrier with the worst channel gain of the assigned users is allocated with the highest power using Worst Sub-carrier First Algorithm (WSFA). In the intra-level PA algorithm, the iterative algorithm Bisection Method (BM) allocates power to each user. Simulation results show that the suggested algorithms improved the sum rate, SE, fairness performance, and outage probabilities when compared to the existing NOMA Resource Allocation (RA) scheme. [ABSTRACT FROM AUTHOR]

Facing uncertain future customer orders, a pull-based available-to-promise (ATP) mechanism will deteriorate the overall profit since it allocates critical resources only to current customer orders. To prevent current less-profitable customer orders from over-consuming critical resources, this study investigates a push–pull based ATP problem with two time stages and three profit margin levels, and develops a dynamic resource reservation policy to maximise the expected total profit. Then, a corresponding push–pull based stochastic ATP model is established with known independent demand distributions, and the optimal reservation level is derived by the genetic algorithm to maximise the expected total profit. Finally, a series of simulation experiments are conducted to reveal the impact of some key factors, and the experiment results provide theoretical guidance and implementation methods for companies to maximise overall profits. [ABSTRACT FROM AUTHOR]

In modular integrated construction (MiC), the resource allocation problem (RAP) adopted by construction sites and vehicle routing problem (VRP) adopted by logistics companies are highly interdependent. However, this interdependence has been overlooked in the literature. Thus, the plans determined by each problem cannot be achieved practically. Moreover, there is no existing VRP model suitable for the MiC application. This study aims to propose a VRP model suitable for MiC, investigate the interdependence between the RAP and proposed VRP model, and develop a blockchain system to secure information sharing between the RAP and VRP. The first two objectives are achieved by developing a coordinated system formulated as a leader-follower Stackelberg game model (LFSGM). This model is presented as a bi-level optimisation model and solved using a nested ant colony optimisation-based algorithm. The effectiveness of the LFSGM is validated using a real case study. The results show that using the traditional approach (i.e. a separate RAP and proposed VRP) succeeds in providing a plan for the construction sites but not for the logistics company. In contrast, the LFSGM offers applicable plans for both the construction sites and logistics company. Lastly, some managerial implications are identified and discussed. [ABSTRACT FROM AUTHOR]

This paper introduces the novel concept of ‘experiential fairness’ to investigate how cultural contexts and personal experiences shape individuals’ perceptions of fairness, moving beyond abstract theoretical principles. Utilizing an experimental design and in-depth interviews with temporary workers in Chinese government departments, the research examines the interplay between self-interest, power dynamics, and fairness perceptions in resource allocation decisions. The findings reveal that individuals often prioritize personal gain over systemic fairness or justice. The persistent issue of unequal pay for equal work underscores underlying labor market tensions and limitations within the civil service system, rather than mere injustice. Additionally, while temporary workers experience unfair treatment, their coping strategies are influenced by personal circumstances rather than universal appeals to fairness. This study advocates for recognizing diverse individual motivations in addressing labor systemic inequities and offers nuanced insights to inform fairer labor policymaking. [ABSTRACT FROM AUTHOR]

Centralized and top-down policymaking, resource allocation, and infrastructure concentration have long shaped Iran's industrial landscape. Rooted in its historical governance model and resource-based economic structure, this approach has inadvertently marginalized regions with untapped potential. A regional approach to industrial development is essential to address the disparities among Iranian provinces. While Research and Innovation Strategies for Smart Specialization (RIS3) has gained prominence for promoting specialization within regions and fostering inter-regional synergy, its adoption remains limited in large developing countries with diverse innovation portfolios. This article proposes a mathematical model for RIS3 that extends beyond high-tech industries to encompass medium – and low-tech sectors, acknowledging the full spectrum of innovation capabilities. The output of model's implementation for Iran’s provinces aligns relatively well with regional spatial planning documents, underscoring its reliability. The model's flexibility also allows adaptation to other contexts, making it a valuable tool for policymakers and practitioners seeking tailored strategies. [ABSTRACT FROM AUTHOR]

Climate change has profound impacts on human health and socioeconomic outcomes. This paper studies the long-term effects of extreme temperature after birth on school-aged children’s cognitive development. The results show significant negative effects of low temperature after birth on children, especially for those born in southern China and unsupportive home environment. Nonlinear effects reveal differences across genders, and an exploration of mechanisms suggests that male neonates’ physiological disadvantages and male-biased allocations of resources combine to cause these results. The findings call for tailored interventions for children and more attention from policymakers, governments and caregivers to successfully implement these measures. [ABSTRACT FROM AUTHOR]

AbstractWe address the problem of online resource allocation in a distributed environment where requests arrive dynamically over time at different agents in the network. As each request arrives, the receiving agent must make an immediate decision that incurs a cost and consumes a certain amount of resources. The requests are drawn independently from unknown distributions that are different for each agent. First, we present an Online Consensus Alternating Direction Method of Multipliers (OC-ADMM) algorithm for the dual counterpart of the online distributed resource allocation problem, focusing on the dual variables. Then, we propose an Online Dual Consensus ADMM (ODC-ADMM) algorithm for the primal problem to derive the primal variables from the dual update process in the OC-ADMM algorithm. The ODC-ADMM algorithm exhibits sublinear growth in both regret and expected constraint violation with respect to the time horizon. Furthermore, extensive numerical results on both synthetic and real-world data confirm its effectiveness. [ABSTRACT FROM AUTHOR]